Intelligent Transportation Systems: A Pipe Dream Whose Components May Already Be on the Road

‘Intelligent Transportation Systems’ can mean anything from good exit ramp design to autonomous vehicles that can pack a highway efficiently at high speed without causing accidents. While the utopian picture of smart cars on smart roads saving lives and fuel is still impractical, the evolution of technology designed to make driving more convenient, simpler, and safer on a car-by-car basis is inching us closer to the point that the addition of a few linchpins will connect the pieces…

Summary: “Intelligent Transportation Systems” can mean anything from good exit ramp design to autonomous vehicles that can pack a highway efficiently at high speed without causing accidents. While the utopian picture of smart cars on smart roads saving lives and fuel is still impractical, the evolution of technology designed to make driving more convenient, simpler and safer on a car-by-car basis is inching us closer to the point that the addition of a few linchpins will connect the pieces in a way that delivers most of the promise of ITS.

Oxymoronic as it seems to anyone who has driven on the highways of New York, Boston or Los Angeles, the United States is in the middle of a long, evolutionary development toward intelligent transportation networks. It’s not hard to see why. According to some estimates only about eight percent of the surface of the 4 million miles of state and interstate highways in the U.S. is being used at peak hours. In a strictly regional view, that means some areas of heavily trafficked roads are thinly used at rush hour, while congestion ties up other areas of the same road increasing the cost of fuel, the volume of pollutants pumped into the air and soil, increased stress on commuters and delays in drivers’ work or personal lives. Intelligent Transportation Systems — an omnibus term that includes everything from designing efficient on and off-ramps on metropolitan highway systems to robotic autonomous vehicles — is widely considered to be the answer to many traffic problems.

The utopian end result tends to lean toward the more science-fictional aspects of Intelligent Transportation — autonomous private vehicles navigating themselves and their passengers safely through traffic packed as densely as sardine cans in a packing plant, each slipping safely through traffic controlled by artificial intelligence built into both the vehicles and the roadway. There is a significant psychological disconnect between that theoretical end result and the reality of Intelligent Transportation Systems (ITS). Unlike with most advanced technologies, the disconnect is one that tends to obscure the real, incremental advances that have made many of the benefits of ITS achievable in the short term. While it’s unlikely government-sanctioned national ITS networks will be built, or even designed, during the next five to ten years, intelligence built into private vehicles, GPS systems, cell-phone networks and other systems and infrastructure will continue to inch ITS into the present with relatively little fanfare about the components’ nature as robotic or artificial-intelligence assists for drivers.

The end product of ITS development may very well be intelligent highway networks that help vehicles navigate and control themselves autonomously while drivers and passengers use vehicular networks to connect to the Internet for work or entertainment. More likely, at least over the next 10 to 15 years, is that sensors and automated control features that are currently rare, complex and expensive will be built into personal vehicles and controlled through a single processing unit onto which drivers can install the features that appeal to them personally.

A navigation package for a new car, for example, might include a heads-up display of routing information, location data, information on traffic and road conditions ahead, and information on potential alternate routes. A safety package, likely to be federally mandated as soon as the technology to deliver it is sufficiently inexpensive and reliable, would include radar or laser sensors to monitor 360 degrees around a vehicle and warn the driver of imminent collisions. Investment opportunities exist first in reducing the size, complexity and cost of sensor systems, then in extending through software or networking the applications enabled by them. In October of 2008, for example, Ford announced an advanced accident warning system linked to an emergency automated braking system.

When an obstacle approaches from the front, the warning system alerts the driver, and activates the brakes if the obstacle approaches too rapidly for the driver to respond. The warning and braking systems were extensions on Ford’s existing sensor network and controls that were built to provide active cruise control — a system that allows the driver to set the speed, but reduces speed automatically if radar sensors in the front detect that the vehicle is approaching another too closely. With such a system, plus a standardized central controller installed, vehicle owners or manufacturers could add software and networking capabilities to provide better night vision capabilities, parking assistance, lane-change warnings, point-of-interest information about nearby fuel stations or restaurants downloaded from location-sensitive Web services rather than an aging database on a GPS. Some of these functions are already available, either as expensive options on luxury cars, or as aftermarket products or services.

Building the onboard sensors, computing hardware and networking equipment to link in-vehicle navigational, control and informational systems to both ad hoc local networks and national cellular, WiFi, WiMax or LTE (long term evolution, or 4G) networks, however, provides tremendous opportunities for investors interested in enhancing the safety of roads, the conservation of fossil fuels and a reduction in the congestion of metropolitan areas.

the Big Picture Stanford University associate professor Sebastian Thrun is one of a host of robotics and artificial-intelligence experts who believe making the vehicles and roadways more intelligent can cut transportation costs and increase safety by making more efficient use of the space already dedicated commuter highways. The U.S. “has paved an amazing amount of surface and is not operating it very efficiently,” Thrun told the Los Angeles Times in 2007. “What hasn’t been done is to make cars drive closer together in a safe way. It is absolutely feasible.”

Thrun is talking about is making cars smart enough to follow each other far more closely than it is reasonable or safe for human drivers to attempt. Even if there were no other policy or technologically based improvements in traffic management, Thrun says, having robotic autopilots able to drive safely at high speed and dense (robot-controlled) traffic would eliminate congestion caused by human drivers who behave unpredictably, causing drivers behind them to brake, which sends a ripple of traffic-delaying reactions in the traffic stream. Such random behavior, and drivers’ reactions to it, cause the bulk of traffic jams that are not the result of accidents, weather or other unavoidable impacts on the stream of traffic.

The Texas Transportation Institute, which has been publishing regular studies on urban traffic congestion and its impact for several decades, estimated that traffic jams in 2007 cost the U.S. economy $78 billion in the form of 4.2 billion wasted person hours and 2.9 billion gallons of wasted fuel.

At a more personal level, the average commuter spends 38 hours of travel time and 26 gallons of fuel, costing a total of $710 per person, sitting in traffic. That congestion and the volume of traffic is only due to increase. A 2007 study conducted by Cambridge Systematics, Inc. on behalf of the Dept. of Transportation predicted that demand for freight transport will increase 92 percent between 2004 and 2035, most of which will be accounted for by increases in truck traffic.

The report predicts that truck borne freight will increase from 12 billion tons in 2004 to nearly 26 billion tons in 2035. Other forms of transport, including rail, water and air freight will also increase, but in single-digit percentages, making up a smaller percentage of total freight transport by 2035 than they do even now.

The dream of Thrun and organizations such as the Intelligent Transportation Systems Institute at the Univ. of Minnesota and ITS International, is to eliminate that waste and drastically reduce the number of accidents on highways by making cars able to drive from one point to another dozens or hundreds of miles away, navigating safely through vehicular and pedestrian traffic along the way, with no assistance from the driver.

Though most of the estimations of the potential benefit of Intelligent Traffic Networks focus on reductions in congestion and their cost, a primary goal of ITN is to reduce traffic accidents, injuries and fatalities, as well. According to a 2008 report from the American Automobile Association, which used the TTI data from 2005, each U.S. traffic fatality costs a total of $3,246, 192 (in 2005 dollars) from lost property, earnings, household production, medical costs, emergency services, travel delay, vocational rehabilitation, workplace costs, legal fees and quality of life reductions. Each injury costs $68, 170, the report found. The total cost of deaths and injuries annually is $164.2 billion.

If ITN can reduce that incidence even 10 percent, it will save a total of $16.4 billion one third more than the entire $12.1 billion 2008 budget of the Dept. of Transportation. Studies examining the practicality of ITN in Minnesota and Kansas in 1999 and 2000 predicted that specific intelligent traffic systems could reduce fatalities between 20 percent and 50 percent. Emergency response systems such as GM’s OnStar could halve fatalities by slashing the response times of emergency services. Metering highway ramps and using cameras to enforce intersection right-of-way rules could cut accidents in those areas 20 percent to 40 percent, the studies showed.

A Long Way to Go

Despite the success of vehicles participating in the DARPA Grand Challenge — in which teams of engineers compete to see which can build a vehicle that can safely navigate on public roads to a distant destination with no help from a driver or remote operator — real autonomy in commuter vehicles is at least 25 years away, most researchers agree.

Part of the holdup is technological. Currently the vision, navigation, target-identification and decision-making systems required for a vehicle to drive itself safely either do not exist, or are too unstable and expensive to be practical for civilian vehicles.

The problem is that autonomous navigation requires a level of geographical and situational awareness that can come only from sensors, data sources and wireless networks built along the roads themselves. Developers building unmanned ground vehicles for the U.S. military start with the assumption that their vehicles will not have adequate data on location, road conditions or other factors. That’s one of the reasons most UGVs are still remote piloted to some degree, even if most of the routine steering, speed and other controls are assisted robotically.

Building the kind of intelligence that would allow a vehicle to independently assess a road surface, traffic conditions, fuel load, speed, location in the road, potential obstacles and other factors necessary for them to operate independently is currently far too expensive and complex a task to manage on a large commercial scale. To even attempt it would require roadways be prepared with beacons or robot-identifiable lane markers, updates on traffic conditions, road surface, recommended or required speed in specific lanes and other metadata that does not currently exist. Building a network that would supply that data even on a small fraction of the 5.7 million miles of paved roads in the U.S., is simply too expensive to contemplate even if the technology were available.

Cost Savings Through Intelligent Transport

Advocates in Congress, including Republican Congressman John L. Mica of Florida, ranking Republican on the House Transportation Committee, and the National Highway Traffic Safety Administration have proposed plans to develop intelligent transportation networks. However, both the capital and opportunity costs of the program have been too high.

However, the success of radio transponders along tollways, especially in the Northeast, has demonstrated that transportation authorities are able to experiment with new technology to improve traffic congestion and compliance with traffic regulations, especially if they can improve their own finances in the process.

Electronic toll collection systems save highway-maintenance organization money by reducing the number of human toll-takers needed at booths, and make turnpike revenues far more predictable than had been the case in the past. A 2001 study conducted by the New Jersey Turnpike Authority to track the average delays at toll booths before and after E-Z Pass was installed showed the transponders reduced traffic-related delays at toll plazas by a total of 85 percent, saving 2.1 million hours of waiting time for transponder users and another 750,000 for those without transponders.

Linking individual state systems such as New York’s E-ZPass, FastLane in Massachusetts, Smart Tag in Virginia and I-Pass in Illinois into one cooperative network (Called the E-ZPass Interagency Group) increased the functionality and appeal still further.

Difficult as it is to build a network that can correctly identify each transponder among hundreds of thousands, check the user’s account balance, and approve his or her passage, in the five seconds or so it takes for a car to roll through a toll booth, it is simple compared to building the kind of systems and networks capable of safely navigating a car through the same toll both amidst traffic made up of both robot and human drivers. The ability to identify and track potential obstacles in such a complex situation is far beyond the ability of today’s automotive-capable technology.